Progress Report
Suryansh Agrawal and Utsav Sapkota
12/5/2021
1 paragraph on where you are currently at in your data analysis
To start off, I feel I have completed almost the entire project. If I have to give a percentage to my progress, I would say that, 80% of my project is completed, except the rechecks and going through it from the beginning. This project helped me learn a lot of new skills which were, I guess, very helpful for me as an analyst. As of now, I feel that we have covered a few steps in the data analytics cycle which is, Question, Collect, and Explore. Now, we are in the process of Analyze and Interpret together. To be fair, the same thing happend that was told to us in the class by you, ‘With analysis, your main question/hypothesis can change’. This is what has happened with us. We will be writing the final question and its elaborate answere in our final reports. Thanks for this opportunity.
At least 1 visual using your dataset
Below, I am attaching the Final Report file’s code and our progress till now, which will show the visual
Introduction
The purpose of this project is to analysis the vaccination trends in the United States. It is an effort to compare and give details about vaccinations in the United States which will eventually lead to general information of the entire country. The data set we are using is provided by Centers for Disease Control and Prevention.
We will be looking at the overall US COVID-19 Vaccine administration and vaccine equity data at county level. This data represents all vaccine partners including jurisdictional partner clinics, retail pharmacies, long-term care facilities, dialysis centers, Federal Emergency Management Agency and Health Resources and Services Administration partner sites, and federal entity facilities.
Ethical Consideration
Data Explanation and Exploration
Data Explanation
This data set talks about the US COVID-19 Vaccinations status and consists of information about people from different age groups, state they are living in, county they are living in, Federal Information Processing Standard State Code, Morbidity and Mortality Weekly Report, if the city is metro or not, Dose details, and some variables which are simple calculations of our existing variables which will be helpful in ypur analysis.
One main thing to observe about this data set is that it provides the data in the universal distribution of the age group, but also tells us about the total total percentage of the people of all age groups. Similarly, even the age group division has both, the total count and even their percentage row which makes our job easy to look at the mean, median, and quartiles in the data set for each variable.
For this project, we will be focusing on some particular variables from our data set. Firstly, we will be focusing on the Recip_State variable because of the fact that it will be helpful for us to make a US Map which will be color coded for the percentage of vaccinated people in that particular state. Secondally, we will also be focusing on the Recip_County varaible because we plan on comparing all the Ohio’s county to Licking County, the place we live in, COVID vaccination status. Another variable to focus on is Series_Complete_Pop_Pct because it will help us do most of our analysis because of the fact that that variable is the percentage that we will be used in most of our statistical and visual analysis.
A big fact that we want our readers to understand is that the further report will be not extremely recent and not according to the most recent data. This data set has been taken from the Centers for Disease Control and Prevention and the fact that they update it regularly and we downloaded the data set and working on its analysis from a particular date should be understood. It will be significant enough to generalize our findings because we will be missing on a very small information when compared to the bigger picture. Moreover, we will be adding 2 new variable from another data set which will be latitude and longitude so that we can make our maps easily.
Visual Explanation
# Making a dataset of Licking County
licking <- filter(covid_vac, Recip_County == 'Licking County')
Mean <- c(mean(licking$Series_Complete_12PlusPop_Pct), mean(licking$Series_Complete_18PlusPop_Pct), mean(licking$Series_Complete_65PlusPop_Pct))
Age <- c('12 Plus', '18 Plus', '65 Plus')
mean_age <- data.frame(Mean, Age)
pie(mean_age$Mean, labels = Age, main = 'Mean of vaccinated people on the basis of age')
Answering the questions that tells us about the variation in th vaccinations according to the age groups is one of the big steps of our analysis. Having that as a basic visual will help us move forward and we will know which age group can be focused on.
ggplot(covid_vac, aes(x=Series_Complete_Pop_Pct)) +
geom_histogram(bins = 20) + theme_economist() +
labs(x = 'Percentage', y = 'Count', title = 'Percentage of people who completed the vaccination')
The above graph shows us how, gradually, the US government was successful in distributing the COVID-19 vaccination and how people are still in the process of getting vaccinated. This graph also shows the fact that there still is a long way to go for the entire to be vaccinated for COVID-19. The country still cannot be free from the virus until more people get vaccinated. We see a big bar in the very beginning which simply tells us how the country started with the vaccination process and there were so less people vaccinated. Later we see that the graph gradually falls with the increase in percentage which means that we still have only a few places which are around 40% vaccinated. And a handful have a more than 80% vaccination rate.
Statistical Analysis and Interpretation
Statistical Models
# Correlation between 18 Plus and 65 Plus Vaccinated people
cor(covid_vac$Series_Complete_18PlusPop_Pct, covid_vac$Series_Complete_65PlusPop_Pct)## [1] 0.9551782# Making a scatterplot for better visual explantion
ggplot(covid_vac, aes(x = Series_Complete_18PlusPop_Pct, y = Series_Complete_65PlusPop_Pct)) +
geom_point() +
labs(title = 'Plot for 18 Plus and 65 Plus vaccinated percentage',
x = '18 Plus',
y = '65 Plus') +
theme_economist() +
stat_smooth(method="lm")## `geom_smooth()` using formula 'y ~ x'
# Correlation test for 18 Plus and 65 Plus Vaccinated people
cor.test(covid_vac$Series_Complete_18PlusPop_Pct, covid_vac$Series_Complete_65PlusPop_Pct)##
## Pearson's product-moment correlation
##
## data: covid_vac$Series_Complete_18PlusPop_Pct and covid_vac$Series_Complete_65PlusPop_Pct
## t = 3423.5, df = 1125758, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9550160 0.9553398
## sample estimates:
## cor
## 0.9551782The correlation test suggests a strong positive relation between the 18 Plus people of the United State and the 65 Plus aged people who are vaccinated completely (have second dose of a two-dose vaccine or one dose of a single-dose vaccine based on the jurisdiction and county where recipient lives) p < 2.2e-16, t=-3423.5, df=1125758). This makes complete sense because it is likely that the US government will be vaccinating both the age groups as per the vaccination availability and also because the points on the scatter plot appear to have an upward slope. The true correlation is suggested to fall between 0.9550160 and 0.9553398, a relatively narrow range that’s pretty close to 1. The true Pearson’s correlation coefficient for these two variables was 0.95. The result is statistically significant, and it’s likely to be practically significant as well: 0.95 is a very strong positive correlation showing that 18 PLus Vaccinated and 65 Plus Vaccinated are growm together in the United States.
# Linear Regression for 18 Plus and 65 Plus Vaccinated people
regression <- linear_reg() %>%
set_engine('lm') %>%
fit(Series_Complete_18PlusPop_Pct~Series_Complete_65PlusPop_Pct, data = covid_vac)
summary(regression$fit)##
## Call:
## stats::lm(formula = Series_Complete_18PlusPop_Pct ~ Series_Complete_65PlusPop_Pct,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.993 -3.769 1.339 2.208 46.835
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.338570 0.010913 -122.7 <2e-16 ***
## Series_Complete_65PlusPop_Pct 0.691522 0.000202 3423.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.012 on 1125758 degrees of freedom
## Multiple R-squared: 0.9124, Adjusted R-squared: 0.9124
## F-statistic: 1.172e+07 on 1 and 1125758 DF, p-value: < 2.2e-16# Making a scatterplot for better visual explantion
ggplot(covid_vac, aes(x = Series_Complete_18PlusPop_Pct, y = Series_Complete_65PlusPop_Pct)) +
geom_point() +
labs(title = 'Plot for 18 Plus and 65 Plus vaccinated percentage',
x = '18 Plus',
y = '65 Plus') +
theme_economist() +
stat_smooth(method="lm")## `geom_smooth()` using formula 'y ~ x'
# QQ Plot
res <- rstandard(regression$fit)
ggplot(, aes(sample = res)) +
geom_qq() +
geom_qq_line() +
theme_economist()
labs(title = 'QQ Plot of Resuidals of our Regression',
y = 'Standardized Residuals',
x = 'Normal Scores')## $y
## [1] "Standardized Residuals"
##
## $x
## [1] "Normal Scores"
##
## $title
## [1] "QQ Plot of Resuidals of our Regression"
##
## attr(,"class")
## [1] "labels"This linear regression model looks at the two different age groups of the United States who have been completely (have second dose of a two-dose vaccine or one dose of a single-dose vaccine, based on the jurisdiction and county where recipient lives) vaccinated, 18 Plus and 65 Plus. Like the correlation test, it suggests a positive relationship: for both the age groups the vaccination rate have been increasing by R 7.012. R2 is 0.912, suggesting that 91% of vaccination in both the age groups is similar and growing with time. While this result is statistically significant (p<2.2e-16), we also feel that this test is practically significant. It is logical that the government will provide the vaccine to both the age groups equally to deal with the devastating situation of COVID in the country. Even the plot shows that there is a positive relationship becaus of the line which is havong a positive slope and increase as the vaccine was available. (see the scatter plot with regression line).
EXPLAIN QQ PLOT
Map Visuals
usbbox <- c(left = -125, bottom = 25.75, right = -67, top = 49)
usmap <- get_stamenmap(usbbox, zoom = 5)## Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.ggmap(usmap)
states_map <- map_data('state')
states <- read.csv('statelatlong.csv')The Dataset
# The data set
covid_vacFiguring out the difference in the vaccination progress in Washington, the state with the capital of US, and, the state we all are in as of now, Ohio.
# Making a data set of Ohio
ohio <- filter(covid_vac, Recip_State == "OH")
# Making a data set of Ohio
washington <- filter(covid_vac, Recip_State == 'WA')